Suatu kajian pemencilan dan pengenalpastian mikroflora bakteria daripada kolam air panas telah dilakukan di kolam
air panas Bentong, Pahang. Kebanyakan bakteria yang dipencil dari kolam air panas adalah dari Bacillus sp. kerana ia
mempunyai ciri adaptasi yang membolehkannya hidup di kawasan ekstrem suhu tinggi dengan pembentukan endospora.
Objektif kajian adalah untuk mengenal pasti spesies bakteria selain dari spesies Bacillus sp. yang mampu mandiri dalam
kolam air panas ini. Tiga buah kolam air panas dikenal pasti sebagai A, B dan C. Julat suhu berada antara 36°C-52°C
dan nilai pH antara 7.88-8.65. Kajian ini telah berjaya memencilkan dua isolat bakteria kokus Gram positif bersama
tiga spesies Gram negatif. Spesies Gram positif tersebut adalah dari spesies Streptococcus sp. dan Staphylococcus sp,
manakala Gram negatif pula dikenal pasti sebagai Pseudomonas sp., Klebsiella sp. dan Proteus sp.. Sebagai kesimpulan,
bakteria tak berspora yang bukan Bacillus sp. mampu mandiri dalam kolam air panas.
Suatu kajian mengenai pemencilan dan pengenalpastian mikroflora bakteria dari kolam air panas telah dilakukan di kolam air panas Bentong, Pahang. Kebanyakan bakteria yang dipencil dari kolam air panas adalah dari Bacillus sp. kerana ia mempunyai ciri adaptasi yang membolehkannya hidup di kawasan suhu yang tinggi dengan pembentukan endospora. Objektif kajian adalah untuk mengenal pasti spesies bakteria selain daripada spesies Bacillus sp. yang boleh dipencilkan dari kolam air panas. Tiga buah kolam air panas (A, B dan C) dikenal pasti dengan julat suhunya antara 36°C-52°C dan nilai pH antara 7.88-8.65. Kajian ini telah berjaya memencilkan dua pencilan bakteria kokus Gram positif dan tiga spesies basilus Gram negatif. Spesies Gram positif tersebut adalah dari genus Streptococcus dan Staphylococcus, manakala bagi Gram negatif pula terdapat masalah dalam pengenalpastian dan hanya dikenali sebagai X, XI and XII.
Improving forecasting particularly time series forecasting accuracy, efficiency and precisely become crucial for the authorities to forecast, monitor, and prevent the COVID-19 cases so that its spread can be controlled more effectively. However, the results obtained from prediction models are inaccurate, imprecise as well as inefficient due to linear and non-linear patterns exist in the data set, respectively. Therefore, to produce more accurate and efficient COVID-19 prediction value that is closer to the true COVID-19 value, a hybrid approach has been implemented. Thus, aims of this study is (1) to propose a hybrid ARIMA-SVM model to produce better forecasting results. (2) to investigate in terms of the performance of the proposed models and percentage improvement against ARIMA and SVM models. statistical measurements such as MSE, RMSE, MAE, and MAPE then conducted to verify that the proposed models are better than ARIMA and SVM models. Empirical results with three real datasets of well-known cases of COVID-19 in Malaysia show that, compared to the ARIMA and SVM models, the proposed model generates the smallest MSE, RMSE, MAE and MAPE values for the training and testing datasets, means that the predicted value from the proposed model is closer to the actual value. These results prove that the proposed model can generate estimated values more accurately and efficiently. As compared to ARIMA and SVM, our proposed models perform much better in terms of error reduction percentages for all datasets. This is demonstrated by the maximum scores of 73.12%, 74.6%, 90.38%, and 68.99% in the MAE, MAPE, MSE, and RMSE, respectively. Therefore, the proposed model can be the best and effective way to improve prediction performance with a higher level of accuracy and efficiency in predicting cases of COVID-19.
Homestay ecotourism in Malaysia has been extensively examined in terms of its concepts, approaches, activities, and community engagement. However, a comprehensive assessment of the sustainability factors pertaining to host families remains a critical area awaiting exploration. This is paramount for ensuring the long-term viability of homestays and fostering economic benefits within rural communities. The present study seeks to establish direct subjective measurements for evaluating the interplay between local communities, tourism, and resources in safeguarding sustainable homestays. Utilizing the Delphi approach, this research conducted interviews with 51 experts who were actively involved in six homestays located on the East Coast of Peninsular Malaysia. The objective was to identify key evaluation indicators pertinent to the homestay industry. The findings underscored the pivotal roles played by community resources and tourism in the sustainability of homestays. Additionally, environmental, economic, and social factors emerged as crucial components for maintaining the industry's sustainability. This innovative assessment methodology offers a valuable instrument for enhancing the sustainability of the homestay sector, especially in the wake of the COVID-19 pandemic. By embracing this approach, homestay operators can fortify their sustainable management practices and prepare themselves for future pandemics. This study represents a significant contribution to the field of homestay ecotourism, emphasizing the imperative for continued research in this dynamic domain.